import torch
import torch.nn.functional as F

x_data = torch.Tensor([[1.0], [2.0], [3.0], [4.0]])
y_data = torch.Tensor([[0],[0],[1],[1]])


class LogisticRegressionModel(torch.nn.Module):
    def __init__(self):
        super(LogisticRegressionModel, self).__init__()
        # Linear对象包括weight(w)以及bias(b)两个成员张量
        self.linear = torch.nn.Linear(1, 1)

    def forward(self, x):
        y_pred = torch.sigmoid(self.linear(x))
        return y_pred

model = LogisticRegressionModel()

criterion = torch.nn.BCELoss(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(),lr=0.01)

for epoch in range(1000):
    y_pred = model(x_data)
    loss = criterion(y_pred,y_data)
    print(epoch,loss.item())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

print('w = ', model.linear.weight.item())
print('b = ', model.linear.bias.item())

x_test = torch.Tensor([[5.0]])
y_test = model(x_test)
print('y_pred = ', y_test.data)